The various techniques of instrumental analysis used in the broad field of analytical chemistry have been developed and refined primarily over the last century. Many of these techniques—such as the spectroscopic techniques including atomic absorption spectroscopy, atomic emission spectroscopy, UV-visible spectroscopy, infrared spectroscopy, NMR spectroscopy and Raman spectroscopy, among others—involve complex interactions of electromagnetic radiation with samples, possibly containing unknown substances to be identified or characterized. Other techniques, such as liquid chromatography (LC), gas chromatography (GC), mass spectrometry (MS) and the hybrid techniques of liquid chromatography-mass spectrometry (LC-MS or HPLC-MS), gas-chromatography-mass spectrometry (GC-MS) and others involve the detection and possibly identification or characterization of various substances derived from mixtures of substances, possibly including unknown analytes, as these substances are separated from one another in a chromatographic column.
One common feature of all the above-listed instrumental techniques is the capability, in use, of generating possibly complex graphs—the graphs generally referred to as spectra—of detected intensity versus some other controlled or measured physical quantity, such as time, frequency, wavelength or mass. In this document, the terms “spectroscopy” and “spectrum” are used in a fashion so as to include additional analytical chemical techniques and data that are not strictly concerned with measuring or representing analytical spectra in the electromagnetic realm. Such additional techniques and data include, but are not limited to, mass spectrometry, mass spectra, chromatography and chromatograms (including liquid chromatography, high-performance liquid chromatography and gas chromatography, either with or without coupling to mass spectrograph instrumentation).
Atomic spectroscopic techniques may produce, for each detected element, spectra consisting of multiple lines representing absorption or emission of electromagnetic energy by various electronic transitions of the atomized element. Likewise, molecular spectroscopic techniques may produce spectra of multiple lines or complexly shaped bands representing absorption or emission of electromagnetic energy by various transitions of molecules among or between various excited and/or ground energy states, such energy states possibly being electronic, vibrational or rotational, depending upon the technique employed.
Still further, mass spectrometry techniques may produce complex spectra consisting of several detected peaks, each such peak representing detection of an ion of a particular mass unit. In mass analysis mode, several peaks, of different mlz values (where m represents mass and z represents charge), may be produced as for each ionized species, as a result of both isotopic variation and differing charges. In the various chromatographic techniques, including those techniques (for instance, GC-MS or LC-MS) in which eluting substances are detected by MS as well as those techniques in which detection is by optical spectroscopy, various possibly overlapping peaks of Gaussian or other skewed shapes may be produced as a function of time as the various substances are eluted.
Traditionally, analytical spectroscopy instrumentation has found its greatest use in specialized research or clinical laboratories in which instrument operation and data analysis is performed by personnel who are highly trained and or experienced in the operation and data collection of the particular employed instruments. However, as the use of analytical spectroscopy instrumentation has expanded, in recent years, from specialized research laboratory environments to general industrial, clinical or even public environments for, for instance, high-throughput screening, there has emerged a need to make instrument operation and data collection and interpretation accessible to less highly trained or experienced users. Thus, there is a need for instrument firmware and software to fulfill greater roles in instrument control and data collection, analysis and presentation so as to render overall turnkey high-throughput operation, with minimal user input or intervention.
Historically, in traditional instrumental analysis situations, collected data is analyzed offline with the aid of specialized software. A first step in conventional data analysis procedures is peak picking, so as to identify and quantify spectral peaks. Such chromatographic or spectroscopic peak detection is one of the most important functions performed by any data analysis system. Typically, chromatographic or spectroscopic peak detection software has employed various user-settable parameters, allowing the operator to provide input in the form of initial guesses for peak locations and intensities and subsequently, to “optimize” the results, after execution of some form of fitting routine that employs the operator's guesses as a starting point. Existing methods of peak detection have many adjustable parameters, requiring operator skill and patience in arriving at an acceptable result. Novice or untrained operators will very likely get an incorrect result or no result at all. This typically results in a very time-consuming process, and the “tweaking” by or inexperience of the user often results in data that is not reproducible and suspect. Further, such traditional forms of peak identification are not suitable for high-throughput industrial process monitoring or clinical or other chemical screening operations, in which there may be a requirement to analyze many hundreds or even thousands of samples per day.
Another critical feature in peak detection is integration of the peak area. With regards to many spectra, the area under a resolved peak is proportional to the number of molecules of a particular analyte. Therefore, assessment of the relative abundances of analytes in a sample requires accurate, robust algorithms for peak integration. Prior attempts at providing automated methods (that is, without input of peak parameters by a user or operator) of peak area calculation generally employ algorithms that calculate the area under the graph of the raw spectral data (e.g., by the trapezoidal method of numerical integration) and, as such, may have multiple or inconsistent criteria to determine how far to extend the numerical integration along the flanks of peaks. Also, such prior numerical integration methods handle overlapped peaks poorly, if at all.
From the foregoing discussion, there is a need in the art for reproducible methods of automated detection, location and area calculation of peaks that do not require initial parameter input or other intervention by a user or operator. The present invention addresses such a need.